ECEF position accuracy and reliability in the presence of differential correction latency

Rahman, Farzana; Aghapour, Elahe; Farrell, Jay A.

doi:10.1109/plans.2018.8373430

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Further comparison as a function of the NP-KF threshold γ and the number of measurements affected by outliers are also of interest. A companion paper at this conference [29] discusses and compares KF algorithms and a differential correction computation approach to maintain accuracy in the presence of communication latency.…”

Section: Discussionmentioning

confidence: 99%

Outlier accommodation for meter-level positioning: Risk-averse performance-specified state estimation

Aghapour

Rahman

Farrell

2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Autonomous vehicle operation would be enhanced if a specified level of accuracy could be guaranteed. Risk-averse performance-specified (RAPS) state estimation works within an optimization setting to choose the set of measurements that achieves a performance specification with minimum risk of inclusion of outliers. This paper considers the challenge of preventing outlier measurements from affecting the accuracy and reliability of state estimation, with minimal risk. This paper is that for the first time applies the RAPS approach to the GNSS vehicle state estimation problem, including a review of the theoretical derivation and experimental results. The experimental results utilize real-world Doppler and differential pseudorange data that allows a comparative study with the standard Neyman-Pearson (NP) approach and RAPS state estimation.

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Section: Discussionmentioning

confidence: 99%

Outlier accommodation for meter-level positioning: Risk-averse performance-specified state estimation

Aghapour

Rahman

Farrell

2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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“…In the standard position, velocity, and acceleration (PVA) model, the rover state is

x = {false[p^{⊤}, v^{⊤}, a^{⊤} false]}^{⊤} \in {double-struckR}^{9}

, where p , v , and a represent the rover three‐dimensional position, velocity, and acceleration vectors, respectively, local in tangent frame. The continuous‐time PVA vehicle model is

\dot{x} (t) = [\begin{array}{ccc} 0 & I & 0 \\ 0 & 0 & I \\ 0 & 0 & - λ I \end{array}] x (t) + [\begin{array}{c} 0 \\ 0 \\ I \end{array}] ω_{a} (t),

where

ω_{a} false(t false) \in {double-struckR}^{3}

is modeled as a white Gaussian noise with power spectral density

Q_{a} = σ_{a}^{2} I

.…”

Section: Experiment: Models and Hardware Set Upmentioning

confidence: 99%

“…The corresponding discrete‐time PVA model is given by Equation with

Φ_{k} = [\begin{array}{ccc} I & T I & a_{3} I \\ 0 & I & a_{2} I \\ 0 & 0 & a_{1} I \end{array}], G_{k} = [\begin{array}{c} 0 \\ 0 \\ 0 \end{array}], Γ_{k} \approx [\begin{array}{c} T^{5 false/ 2} false/ \sqrt{20} I \\ T^{3 false/ 2} false/ \sqrt{3} I \\ \sqrt{T} I \end{array}], and Ω_{k} = [\begin{array}{c} 0 \\ 0 \\ σ_{a}^{2} I \end{array}],

where T represents the GPS sampling time and all submatrices are three by three with a 1 = e − λT , a 2 =(1− e − λT )/ λ , a 3 =( λT −1+ e − λT )/ λ 2 , and

ω_{k} \sim scriptN false(0, {normalΩ}_{k} false)

. The approximation indicated in Γ k yields the correct diagonal of the discrete‐time noise covariance matrix, but

Q_{k} = {normalΓ}_{k} {normalΩ}_{k} {normalΓ}_{k}^{⊤}

and approximates the off‐diagonal terms relative to the exact calculation.…”

Section: Experiment: Models and Hardware Set Upmentioning

confidence: 99%

Outlier accommodation in moving‐horizon state estimation: A risk‐averse performance‐specified approach

Aghapour

Farrell

2019

Adaptive Control & Signal

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Summary This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman‐Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer‐specified threshold. When measurements with outliers are used (ie, missed detections), both the state estimate and the error covariance matrix become corrupted. This corrupted state and covariance estimate are then the basis for all subsequent outlier decisions. When valid measurements are rejected (ie, false alarms), potentially using the corrupted state estimate and error covariance, measurement information is lost. Either using invalid information or discarding too much valid information can result in divergence of the KF. An alternative approach is moving‐horizon (MH) state estimation, which maintains all recent measurement data within a moving window with a time horizon of length L. In MH approaches, the number of measurements available for state estimation is affected by both the number of measurements per time step and the number of time steps L over which measurements are retained. Risk‐averse performance‐specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This paper derives and formulates the MH‐RAPS solution for outlier accommodation. The paper also presents implementation results. The MH‐RAPS application uses Global Navigation Satellite Systems measurements to estimate the state of a moving platform using a position, velocity, and acceleration model. In this application, MH‐RAPS performance is compared with MH‐NP state estimation.

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Earth-Centered Earth-Fixed (ECEF) Vehicle State Estimation Performance

Rahman¹,

Farrell²

2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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ECEF position accuracy and reliability in the presence of differential correction latency

Cited by 6 publications

References 23 publications

Outlier accommodation for meter-level positioning: Risk-averse performance-specified state estimation

Outlier accommodation for meter-level positioning: Risk-averse performance-specified state estimation

Outlier accommodation in moving‐horizon state estimation: A risk‐averse performance‐specified approach

Earth-Centered Earth-Fixed (ECEF) Vehicle State Estimation Performance

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